Surrogate assisted local search in PMSM drive design

Neri, Ferrante; García, Xavier; Cascella, Giuseppe Leonardo; Salvatore, N.

doi:10.1108/03321640810861043

Cited by 29 publications

(15 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It is still among the first choices for researchers in need of a deterministic local search. The most recent applications include Neri et al [34] as well as Benasla et al [5]. The former used the HJ pattern search to optimise the design parameters of a controller for an electric engine, the latter to find the optimal power production for a given grid system.…”

Section: Eo With Hooke-jeeves Pattern Searchmentioning

confidence: 99%

Dynamic function optimisation with hybridised extremal dynamics

Moser

Chiong

2009

Memetic Comp.

View full text Add to dashboard Cite

Dynamic function optimisation is an important research area because many real-world problems are inherently dynamic in nature. Over the years, a wide variety of algorithms have been proposed to solve dynamic optimisation problems, and many of these algorithms have used the Moving Peaks (MP) benchmark to test their own capabilities against other approaches. This paper presents a detailed account of our hybridised Extremal Optimisation (EO) approach that has achieved hitherto unsurpassed results on the three standardised scenarios of the MP problem. Several different components are used in the hybrid EO, and it has been shown that a large proportion of the quality of its outstanding performance is due to the local search component. In this paper, the behaviour of the local search algorithms used is analysed, and the roles of other components are discussed. In the concluding remarks, the generalisation ability of this method and its wider applicability are highlighted.

show abstract

Section: Eo With Hooke-jeeves Pattern Searchmentioning

confidence: 99%

Dynamic function optimisation with hybridised extremal dynamics

Moser

Chiong

2009

Memetic Comp.

View full text Add to dashboard Cite

show abstract

“…-A jDE according to the implementation and parameter setting given in [47]. Thus, with reference to formulas (5) and (6), it has been run with F l = 0.1, F u = 0.9, and τ 1 = τ 2 = 0.1. -The Durations Sizing Genetic Algorithm (DSGA) proposed in [16].…”

Section: Numerical Resultsmentioning

confidence: 99%

“…For example, [22] and [23] propose taking the fitness estimates of neighboring individuals in order to predict the fitness value of some candidate solutions. Paper [5], by employing a similar philosophy, proposes construction of a local linear surrogate model (an approximate model of the true fitness function) which locally performs the noise filtering.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A memetic Differential Evolution approach in noisy optimization

Mininno

Neri

2009

Memetic Comp.

Self Cite

View full text Add to dashboard Cite

This paper proposes a memetic approach for solving complex optimization problems characterized by a noisy fitness function. The proposed approach aims at solving highly multivariate and multi-modal landscapes which are also affected by a pernicious noise. The proposed algorithm employs a Differential Evolution framework and combines within this three additional algorithmic components. A controlled randomization of scale factor and crossover rate are employed which should better handle uncertainties of the problem and generally enhance performance of the Differential Evolution. Two combined local search algorithms applied to the scale factor, during offspring generation, should enhance performance of the Differential Evolution framework in the case of multi-modal and high dimensional problems. An on-line statistical test aims at assuring that only strictly necessary samples are taken and that all pairwise selections are properly performed. The proposed algorithm has been tested on a various set of test problems and its behavior has been studied, dependent on the dimensionality and noise level. A comparative analysis with a standard Differential Evolution, a modern version of Differential Evolution employing randomization of the control parameters and four metaheuristics tailored to optimization in a noisy environment has been carried out. One of these metaheuristics is a classical algorithm for noisy optimization while the other three are modern Differential Evolution based algorithms for noisy optimization which well represent the state-of-theart in the field. Numerical results show that the proposed memetic approach is an efficient and robust alternative for various and complex multivariate noisy problems and can be exported to real-world problems affected by a noise whose distribution can be approximated by a Gaussian distribution.

show abstract

“…In terms of its computational behaviour, HAGA can be regarded as a surrogate assisted strategy, e.g. see [61] and [69], that operates in the multi-dimensional objective space. For example a population selection for WFG1 for fifteen objectives can be solved by HAGA more than five million times faster that CHV selection still attaining a very good approximation of the theoretical HV accuracy (0.05871 instead of 0.05879), see Table 7.…”

Section: Resultsmentioning

confidence: 99%

A fast hypervolume driven selection mechanism for many-objective optimisation problems

Rostami

Neri

2017

Swarm and Evolutionary Computation

Self Cite

View full text Add to dashboard Cite

Solutions to real-world problems often require the simultaneous optimisation of multiple conflicting objectives. In the presence of four or more objectives, the problem is referred to as a "many-objective optimisation problem". A problem of this category introduces many challenges, one of which is the e↵ective and e cient selection of optimal solutions.The hypervolume indicator (or s-metric), i.e. the size of dominated objective space, is an e↵ective selection criterion for many-objective optimisation. The indicator is used to measure the quality of a nondominated set, and can be used to sort solutions for selection as part of the contributing hypervolume indicator. However, hypervolume based selection methods can have a very high, if not infeasible, computational cost.The present study proposes a novel hypervolume driven selection mechanism for many-objective problems, whilst maintaining a feasible computational cost. This approach, named the Hypervolume Adaptive Grid Algorithm (HAGA), uses two-phases (narrow and broad) to prevent population-wide calculation of the contributing hypervolume indicator. Instead, HAGA only calculates the contributing hypervolume indicator for grid populations, i.e. for a few solutions, which are close in proximity (in the objective space) to a candidate solution when in competition for survival. The result is a trade-o↵ between complete accuracy in selecting the fittest individuals in regards to hypervolume quality, and a feasible computational time in many-objective space. The real-world e ciency of the proposed selection mechanism is demonstrated within the optimisation of a classifier for concealed weapon detection.

show abstract

Surrogate assisted local search in PMSM drive design

Cited by 29 publications

References 21 publications

Dynamic function optimisation with hybridised extremal dynamics

Dynamic function optimisation with hybridised extremal dynamics

A memetic Differential Evolution approach in noisy optimization

A fast hypervolume driven selection mechanism for many-objective optimisation problems

Contact Info

Product

Resources

About